Applied Scientist, Japan Store Tech

Amazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems.

As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times.



Key job responsibilities
- Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems.
- Analyzing data and metrics relevant to the search experiences.
- Working with teams worldwide on global projects.

Your benefits include:
- Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers
- The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact
- Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals

We are open to hiring candidates to work out of one of the following locations:

Beijing, 11, CHN | Shanghai, 31, CHN